In his new book WTF? What’s the Future and Why It’s Up to Us, technology guru and media publisher Tim O’Reilly highlights lessons learned from data-driven platform companies and what they have to say about the future of business and the future of the economy.
During a keynote talk at the recent Strata Data Conference, O’Reilly highlighted five lessons from his book, including that platform companies rely on algorithmic systems that have objective functions. That’s good and bad. Objective functions do exactly what humans tell them to do, which can lead to biases and create inequality. But, as humans learn to build better algorithms, the software’s objectivity could translate into a more harmonious future for the human race. (O’Reilly’s glass, in this case, is definitely half full.)
Platform companies like Uber, Google, Amazon and Facebook rely on algorithmic systems to connect people in real time using predictive analytics and sensor data. Part of their success is that they’ve relentlessly pursued the new and in the process redefined business models.
Case in point: Taxicab companies often point to the mobile apps from Uber and Lyft as the competitive differentiator, ignoring how the platform companies have upended a crucial part of the old business model. When demand for cabs was high, supply couldn’t scale. Thanks to technology and “cognitive augmentation” tools like Google Maps, supply of Uber and Lyft drivers scales right along with demand, and drivers can find passengers in minutes, according to O’Reilly.
And it’s not that taxicab companies haven’t tried to innovate. “We actually had connected taxicabs in 2005,” he said. The livery industry installed screens in the backseats of cabs, but the screens were — and still are — there to show advertisements to captive passengers. This is an example of new technology pasted onto an old business model — not an example of how technology infuses a business model, O’Reilly said.
Tim O’Reilly talks about his new book WTF? What’s the Future and Why It’s Up to Us at the Strata Data Conference.
Algorithmic systems gone awry
Yet despite the growth platform companies are experiencing, technology still carries a stigma for many of us. O’Reilly has a theory about why that is: Algorithmic systems can — and do — go sideways. Other keynote speakers at Strata also highlighted that fact. Sam Lavigne, artist and programmer, talked about the dangers of predictive policing applications that rely on historical (and perhaps biased) data to make predictions about future crime. “Typical policing methodologies tend to criminalize poverty,” he said. “And, therefore, typical predictive policing apps will also criminalize poverty.”
These algorithms do exactly what we tell them to do. But we don’t always understand what we told them to do. Tim O’Reillyfounder, O’Reilly Media
Or consider O’Reilly’s example of the role Facebook played in the 2016 presidential election. The social media company has algorithmic systems optimized for getting users engaged with content, but it didn’t understand all of the nuances of engagement or how people might try to subvert the system, according to O’Reilly. “So they ended up with hyper-partisanship in the last election,” he said.
The algorithms took on the biases of the user, delivering content that reflected their likes — and dislikes. Algorithmic systems, he argued, are a little like the genies of Arabian mythology. “These algorithms do exactly what we tell them to do. But we don’t always understand what we told them to do,” he said.
Part of the problem is that developers don’t know how to talk to algorithms and ask for the right wish, he said. Consider the financial markets, which today are vast algorithmic systems with a master objective function to increase profits.
“The idea was that this would allow businesses to share those profits with shareholders who would use [them] in a socially conscious way,” he said. “But it didn’t work out that way.” Instead, financiers are gaming the system, creating income inequality.
But algorithmic systems don’t have to operate that way. O’Reilly, for one, remains optimistic, believing that algorithmic systems hold up a mirror to humanity and will become a driving force in creating a better world. “Bias in code taken to scale becomes visible,” he said. “When we see that we have encoded decades of biased policing into the data that we feed our predictive policing algorithms, we correct not just our AI, not just our algorithms — we can see ourselves.”
During his presentation at the Strata Data Conference, Sam Lavigne shined a light on the potential for bias in predictive policing applications.
Businesses can’t stop with the first iteration — screens in the back of taxicabs weren’t the futu
re. They have to seek out the new, learn from their mistakes and figure out how to achieve their real objectives, O’Reilly said.
“This will eventually become a political process. But it’s fundamentally right now, a process for all of us in business to understand our values, and what are we encoding into the systems we build,” he said. The operative question for us: “What is the wish we’re giving the genie we are about to unleash onto the world?”
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